AI Factories Will Become More And More Popular

AI accelerates the transition from the manual age to the industrial age, and the expansion of the infrastructure market also drives the development of AI.

What is an AI factory?

Today, the most critical AI technologies are machine learning algorithms. Today’s AI algorithms have become important solution components in fields such as healthcare, finance, manufacturing, and transportation.

The essence of the algorithm is the statistical engine, responsible for collecting patterns from previously observed data and predicting new results based on it. The fusion of machine learning algorithms with other key components (including data sources, experiments, and software) creates an AI factory, representing a set of components and processes that connect to each other to facilitate learning and development.

Obtain high-quality data from internal and external sources to train machine learning algorithms, which are then used to perform predictions on specific tasks.

In some cases, such as disease diagnosis and treatment, such predictions can help human experts make accurate judgments.

The algorithms and data-driven models of AI Factory allow organizations of all kinds to quickly test hypotheses and then introduce changes to continuously improve their systems. In essence, the AI ​​factory establishes a complete benign closed loop between user participation, data collection, algorithm design, prediction and improvement.

A global AI factory is being built

Over the past few years, a lot of sensors have been installed in the factory, with facial recognition and Flap Barrier Gates installed at the entrance and exit. But from this year, artificial intelligence began to enter factories, and machines began to learn to understand the daily activities of industrial production. Data-based industrial production becomes smarter in the future.

The AI ​​factory is the most evolved version of the smart factory that is spreading all over the world recently. AI factories are factories that combine traditional manufacturing factories and information technology (IT) to improve production efficiency.

When the stock of raw materials needed to manufacture products is insufficient, AI will automatically buy large quantities when the market price is the lowest. If AI is used on the production line of a general manufacturing plant, it can immediately find out where the problem is when the production speed is slowed down for 1 second, give tired workers a break or instruct the machine to repair.

In the past few years, with the investment of global research teams in algorithm optimization, artificial intelligence applications have become an important core for enterprises to solve operational difficulties, optimize decision-making, provide financial services for consumers, and even realize the vision of intelligent manufacturing in the manufacturing industry.

The use of artificial intelligence can help factory managers greatly improve decision-making efficiency and help traditional manufacturing to achieve transformation and upgrading. 5G plays a key role in the application of artificial intelligence.

In a factory of the future like this, artificial intelligence is like the brain, and 5G is like the nerve. With the rush of Industry 4.0 from Germany to the world and the implementation of “Made in China 2025”, more and more domestic manufacturing companies have begun to implement digital transformation.

Use emerging technologies such as artificial intelligence, Internet of Things, big data and cloud computing to upgrade functions such as factories to gain the ability to quickly respond to the market, maximize production efficiency and save costs.

Transforming your business with AI

Inside the factory, AI will bring various benefits to production as well as maintenance, quality and logistics.

Production: In environments such as continuous processing and discrete production, manufacturers are using AI to reduce costs and increase speed, thereby increasing productivity.

Maintenance: Manufacturers will use AI to reduce equipment failures and improve asset utilization. AI will continuously analyze and learn data generated by machines and components.

Quality: Manufacturers can use AI to help detect quality issues early. Vision systems use image recognition technology to identify defects and deviations in product functionality; they also continuously analyze and learn from data generated by machines and production environments.

Logistics: This logistics refers to intra-production logistics and warehousing, not the logistics of external supply chains. AI will facilitate the automated transfer and efficiency of on-site material supply, which is necessary to manage the growing complexity of manufacturing multiple product derivatives and custom products.

Reporting: The AI ​​system recommends solutions to incidents based on incident reports, and continuously analyzes and learns from these reports.

Today, American companies have widespread adoption of AI technology. Even so, China has overtaken the U.S. in AI investment, accounting for nearly half of global investment in AI startups last year.

In 2017, China’s State Council also promulgated the “Next Generation Artificial Intelligence Development Plan”, which intends to use a three-step strategy to reach the world’s leading AI level by 2030; China’s Tianjin Municipal Government has set up a 30 billion yuan fund to support the AI ​​industry.

Other emerging countries, such as India, have taken a similar attitude, seeing AI adoption as a necessary element to maintain the global competitiveness of their manufacturing industries, and investing heavily in AI.

Problems that AI factories need to solve

Machine learning algorithms rely heavily on large amounts of data, but large amounts of data alone do not constitute a good AI algorithm. Many companies are sitting on massive data stores, but their data and software exist in separate silos with inconsistent storage and incompatible models and frameworks.

Even though customers view the enterprise as a unified entity, internally, systems and data are often fragmented across departments and functions. This prevents the aggregation of data, delays the generation of insights, and makes it impossible to harness the power of analytics and artificial intelligence.

Additionally, data must be preprocessed before it can be fed to AI algorithms. Even when dealing with structured data such as sales records, there can be gaps, missing information, and other inaccuracies that need to be addressed.

Among others, such as establishing the right metrics and capabilities for supervised machine learning algorithms, finding the right gap between human expert insights and AI predictions, and tackling the challenges of running experiments and validating the results.

Summarize:

However, compared with industries such as finance, although there are many application scenarios of artificial intelligence in the manufacturing industry, it is not prominent, and it can even be considered that the development is slow.

At present, as more and more enterprises enter the field of artificial intelligence, a large number of successful artificial intelligence open source software and platforms continue to pour in, and the AI ​​factory will usher in an unprecedented period of outbreak.

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